Balancing the impacts of public policy on people’s health and their wallets has become even more relevant since the onset of the COVID-19 pandemic. Stay-at-home orders were widely implemented in the US to combat the spread of the virus, but their benefits came at costs to employment, earnings, and spending felt by millions of people.
Purdue University economics professor Mario J. Crucini and Vanderbilt University coauthor Oscar O’Flaherty found a 4-percentage point decrease in consumer spending and hours worked caused by the implementation of stay-at-home orders in March 2020. Their final estimates reflected nearly a $10 billion decrease in consumer spending and more than a $15 billion loss in employee earnings. They also found that 11.6 percent of lost jobs were attributed to stay-at-home orders.
Their research working paper, “Stay-at-Home Orders in a Fiscal Union,” quantifies the economic impact of stay-at-home orders. Combining data from the time-clock company Homebase and county level data for every US state from the non-profit Opportunity Insights, they estimate labor-market and employment effects of COVID’s geographic spread and the role of stay-at-home orders. Spending data from Affinity Solutions was used to measure consumer spending.
Not only did Crucini and his colleagues quantify the problem, they also developed a model to analyze virus transmission and possible solutions. They used this model to evaluate the best policies when infection rates are different in different locations. They assume that the virus is not fully transmitted across locations, just like the conditions at the onset of the pandemic.
“There is no scenario where the health consequences or the economic consequences are zero,” Crucini says. “There is a very unpleasant tradeoff. But if we wait long enough and practice prudent health protocol, we get a better tradeoff.”
The optimal infection mitigation policies, they found, were different for severely infected states versus mildly infected states for almost 20 weeks. This means that a nationwide mandate is probably too restrictive for the economy of mildly infected states.
In other words, when states have different rates of infection and partial transmission, policy choices should be different as well.
“The point our paper makes is that the choice should vary across locations based upon the epidemiological evidence both locally and more broadly,” Crucini says. “Areas with higher infection rates would have more restrictive policies than those with lower infection rates.”
The paper highlights the importance of using data to develop public policy decisions that balance health and economic outcomes. Since statewide stay-at-home orders affected 90 percent of the US population in the span of three weeks, it is clear that these findings are critical when shaping policy going forward.